Exploiting Non-uniform Inherent Cues to Improve Presentation Attack Detection

Face anti-spoofing plays a vital role in face recognition systems. The existed deep learning approaches have effectively improved the performance of presentation attack detection (PAD). However, they learn a uniform feature for different types of presentation attacks, which ignore the diversity of the inherent cues presented in different spoofing types. As a result, they can not effectively represent the intrinsic difference between different spoof faces and live faces, and the performance drops on the cross-domain databases. In this paper, we introduce the inherent cues of different spoofing types by non-uniform learning as complements to uniform features. Two lightweight sub-networks are designed to learn inherent motion patterns from photo attacks and the inherent texture cues from video attacks. Furthermore, an element-wise weighting fusion strategy is proposed to integrate the non-uniform inherent cues and uniform features. Extensive experiments on four public databases demonstrate that our approach outperforms the state-of-the-art methods and achieves a superior performance of 3.7% ACER in the cross-domain Protocol 4 of the Oulu-NPU database. Code is available at https://github.com/BJUT-VIP/Non-uniform-cues.